Endogenous vasopressin and the central control of heart rate during dynamic exercise

The present article contains a brief review on the role of vasopressinergic projections to the nucleus tractus solitarii in the genesis of reflex bradycardia and in the modulation of heart rate control during exercise. The effects of vasopressin on exercise tachycardia are discussed on the basis of both the endogenous peptide content changes and the heart rate response changes observed during running in sedentary and trained rats. Dynamic exercise caused a specific vasopressin content increase in dorsal and ventral brainstem areas. In accordance, rats pretreated with the peptide or the V1 blocker into the nucleus tractus solitarii showed a significant potentiation or a marked blunting of the exercise tachycardia, respectively, without any change in the pressure response to exercise. It is proposed that the long-descending vasopressinergic pathway to the nucleus tractus solitarii serves as one link between the two main neural controllers of circulation, i.e., the central command and feedback control mechanisms driven by the peripheral receptors. Therefore, vasopressinergic input could contribute to the adjustment of heart rate response (and cardiac output) to the circulatory demand during exercise.

Stiffness based trajectory planning and feedforward based vibration suppression control of parallel robot machines

The dissertation proposes two control strategies, which include the trajectory planning and vibration suppression, for a kinematic redundant serial-parallel robot machine, with the aim of attaining the satisfactory machining performance. For a given prescribed trajectory of the robot's end-effector in the Cartesian space, a set of trajectories in the robot's joint space are generated based on the best stiffness performance of the robot along the prescribed trajectory. To construct the required system-wide analytical stiffness model for the serial-parallel robot machine, a variant of the virtual joint method (VJM) is proposed in the dissertation. The modified method is an evolution of Gosselin's lumped model that can account for the deformations of a flexible link in more directions. The effectiveness of this VJM variant is validated by comparing the computed stiffness results of a flexible link with the those of a matrix structural analysis (MSA) method. The comparison shows that the numerical results from both methods on an individual flexible beam are almost identical, which, in some sense, provides mutual validation. The most prominent advantage of the presented VJM variant compared with the MSA method is that it can be applied in a flexible structure system with complicated kinematics formed in terms of flexible serial links and joints. Moreover, by combining the VJM variant and the virtual work principle, a systemwide analytical stiffness model can be easily obtained for mechanisms with both serial kinematics and parallel kinematics. In the dissertation, a system-wide stiffness model of a kinematic redundant serial-parallel robot machine is constructed based on integration of the VJM variant and the virtual work principle. Numerical results of its stiffness performance are reported. For a kinematic redundant robot, to generate a set of feasible joints' trajectories for a prescribed trajectory of its end-effector, its system-wide stiffness performance is taken as the constraint in the joints trajectory planning in the dissertation. For a prescribed location of the end-effector, the robot permits an infinite number of inverse solutions, which consequently yields infinite kinds of stiffness performance. Therefore, a differential evolution (DE) algorithm in which the positions of redundant joints in the kinematics are taken as input variables was employed to search for the best stiffness performance of the robot. Numerical results of the generated joint trajectories are given for a kinematic redundant serial-parallel robot machine, IWR (Intersector Welding/Cutting Robot), when a particular trajectory of its end-effector has been prescribed. The numerical results show that the joint trajectories generated based on the stiffness optimization are feasible for realization in the control system since they are acceptably smooth. The results imply that the stiffness performance of the robot machine deviates smoothly with respect to the kinematic configuration in the adjacent domain of its best stiffness performance. To suppress the vibration of the robot machine due to varying cutting force during the machining process, this dissertation proposed a feedforward control strategy, which is constructed based on the derived inverse dynamics model of target system. The effectiveness of applying such a feedforward control in the vibration suppression has been validated in a parallel manipulator in the software environment. The experimental study of such a feedforward control has also been included in the dissertation. The difficulties of modelling the actual system due to the unknown components in its dynamics is noticed. As a solution, a back propagation (BP) neural network is proposed for identification of the unknown components of the dynamics model of the target system. To train such a BP neural network, a modified Levenberg-Marquardt algorithm that can utilize an experimental input-output data set of the entire dynamic system is introduced in the dissertation. Validation of the BP neural network and the modified Levenberg- Marquardt algorithm is done, respectively, by a sinusoidal output approximation, a second order system parameters estimation, and a friction model estimation of a parallel manipulator, which represent three different application aspects of this method.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Cardiovascular control in experimental diabetes

Several studies have reported impairment in cardiovascular function and control in diabetes. The studies cited in this review were carried out from a few days up to 3 months after streptozotocin administration and were concerned with the control of the circulation. We observed that early changes (5 days) in blood pressure control by different peripheral receptors were maintained for several months. Moreover, the impairment of reflex responses observed after baroreceptor and chemoreceptor stimulation was probably related to changes in the efferent limb of the reflex arc (sympathetic and parasympathetic), but changes also in the central nervous system could not be excluded. Changes in renal sympathetic nerve activity during volume expansion were blunted in streptozotocin-treated rats, indicating an adaptive natriuretic and diuretic response in the diabetic state. The improvement of diabetic cardiovascular dysfunction induced by exercise training seems to be related to changes in the autonomic nervous system. Complementary studies about the complex interaction between circulation control systems are clearly needed to adequately address the management of pathophysiological changes associated with diabetes.

Influence of the neural tube/notochord complex on MyoD expression and cellular proliferation in chicken embryos

Important advances have been made in understanding the genetic processes that control skeletal muscle formation. Studies conducted on quails detected a delay in the myogenic program of animals selected for high growth rates. These studies have led to the hypothesis that a delay in myogenesis would allow somitic cells to proliferate longer and consequently increase the number of embryonic myoblasts. To test this hypothesis, recently segmented somites and part of the unsegmented paraxial mesoderm were separated from the neural tube/notochord complex in HH12 chicken embryos. In situ hybridization and competitive RT-PCR revealed that MyoD transcripts, which are responsible for myoblast determination, were absent in somites separated from neural tube/notochord (1.06 and 0.06 10-3 attomol MyoD/1 attomol ß-actin for control and separated somites, respectively; P<0.01). However, reapproximation of these structures allowed MyoD to be expressed in somites. Cellular proliferation was analyzed by immunohistochemical detection of incorporated BrdU, a thymidine analogue. A smaller but not significant (P = 0.27) number of proliferating cells was observed in somites that had been separated from neural tube/notochord (27 and 18 for control and separated somites, respectively). These results confirm the influence of the axial structures on MyoD activation but do not support the hypothesis that in the absence of MyoD transcripts the cellular proliferation would be maintained for a longer period of time.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Engagement of multifocal neural circuits during recall of autobiographical happy events

Happy emotional states have not been extensively explored in functional magnetic resonance imaging studies using autobiographic recall paradigms. We investigated the brain circuitry engaged during induction of happiness by standardized script-driven autobiographical recall in 11 healthy subjects (6 males), aged 32.4 ± 7.2 years, without physical or psychiatric disorders, selected according to their ability to vividly recall personal experiences. Blood oxygen level-dependent (BOLD) changes were recorded during auditory presentation of personal scripts of happiness, neutral content and negative emotional content (irritability). The same uniform structure was used for the cueing narratives of both emotionally salient and neutral conditions, in order to decrease the variability of findings. In the happiness relative to the neutral condition, there was an increased BOLD signal in the left dorsal prefrontal cortex and anterior insula, thalamus bilaterally, left hypothalamus, left anterior cingulate gyrus, and midportions of the left middle temporal gyrus (P < 0.05, corrected for multiple comparisons). Relative to the irritability condition, the happiness condition showed increased activity in the left insula, thalamus and hypothalamus, and in anterior and midportions of the inferior and middle temporal gyri bilaterally (P < 0.05, corrected), varying in size between 13 and 64 voxels. Findings of happiness-related increased activity in prefrontal and subcortical regions extend the results of previous functional imaging studies of autobiographical recall. The BOLD signal changes identified reflect general aspects of emotional processing, emotional control, and the processing of sensory and bodily signals associated with internally generated feelings of happiness. These results reinforce the notion that happiness induction engages a wide network of brain regions.

Vascular adaptive responses to physical exercise and to stress are affected differently by nandrolone administration

Androgenic anabolic steroid, physical exercise and stress induce cardiovascular adaptations including increased endothelial function. The present study investigated the effects of these conditions alone and in combination on the vascular responses of male Wistar rats. Exercise was started at 8 weeks of life (60-min swimming sessions 5 days per week for 8 weeks, while carrying a 5% body-weight load). One group received nandrolone (5 mg/kg, twice per week for 8 weeks, im). Acute immobilization stress (2 h) was induced immediately before the experimental protocol. Curves for noradrenaline were obtained for thoracic aorta, with and without endothelium from sedentary and trained rats, submitted or not to stress, treated or not with nandrolone. None of the procedures altered the vascular reactivity to noradrenaline in denuded aorta. In intact aorta, stress and exercise produced vascular adaptive responses characterized by endothelium-dependent hyporeactivity to noradrenaline. These conditions in combination did not potentiate the vascular adaptive response. Exercise-induced vascular adaptive response was abolished by nandrolone. In contrast, the aortal reactivity to noradrenaline of sedentary rats and the vascular adaptive response to stress of sedentary and trained rats were not affected by nandrolone. Maximum response for 7-10 rats/group (g): sedentary 3.8 ± 0.2 vs trained 3.0 ± 0.2*; sedentary/stress 2.7 ± 0.2 vs trained/stress 3.1 ± 0.1*; sedentary/nandrolone 3.6 ± 0.1 vs trained/nandrolone 3.8 ± 0.1; sedentary/stress/nandrolone 3.2 ± 0.1 vs trained/stress/nandrolone 2.5 ± 0.1*; *P < 0.05 compared to its respective control. Stress and physical exercise determine similar vascular adaptive response involving distinct mechanisms as indicated by the observation that only the physical exercise-induced adaptive response was abolished by nandrolone.

Neural regulation of the stress response: glucocorticoid feedback mechanisms

The mammalian stress response is an integrated physiological and psychological reaction to real or perceived adversity. Glucocorticoids are an important component of this response, acting to redistribute energy resources to both optimize survival in the face of challenge and to restore homeostasis after the immediate challenge has subsided. Release of glucocorticoids is mediated by the hypothalamo-pituitary-adrenal (HPA) axis, driven by a neural signal originating in the paraventricular nucleus (PVN). Stress levels of glucocorticoids bind to glucocorticoid receptors in multiple body compartments, including the brain, and consequently have wide-reaching actions. For this reason, glucocorticoids serve a vital function in negative feedback inhibition of their own secretion. Negative feedback inhibition is mediated by a diverse collection of mechanisms, including fast, non-genomic feedback at the level of the PVN, stress-shut-off at the level of the limbic system, and attenuation of ascending excitatory input through destabilization of mRNAs encoding neuropeptide drivers of the HPA axis. In addition, there is evidence that glucocorticoids participate in stress activation via feed-forward mechanisms at the level of the amygdala. Feedback deficits are associated with numerous disease states, underscoring the necessity for adequate control of glucocorticoid homeostasis. Thus, rather than having a single, defined feedback ‘switch’, control of the stress response requires a wide-reaching feedback ‘network’ that coordinates HPA activity to suit the overall needs of multiple body systems.

Mapping and signaling of neural pathways involved in the regulation of hydromineral homeostasis

Several forebrain and brainstem neurochemical circuitries interact with peripheral neural and humoral signals to collaboratively maintain both the volume and osmolality of extracellular fluids. Although much progress has been made over the past decades in the understanding of complex mechanisms underlying neuroendocrine control of hydromineral homeostasis, several issues still remain to be clarified. The use of techniques such as molecular biology, neuronal tracing, electrophysiology, immunohistochemistry, and microinfusions has significantly improved our ability to identify neuronal phenotypes and their signals, including those related to neuron-glia interactions. Accordingly, neurons have been shown to produce and release a large number of chemical mediators (neurotransmitters, neurohormones and neuromodulators) into the interstitial space, which include not only classic neurotransmitters, such as acetylcholine, amines (noradrenaline, serotonin) and amino acids (glutamate, GABA), but also gaseous (nitric oxide, carbon monoxide and hydrogen sulfide) and lipid-derived (endocannabinoids) mediators. This efferent response, initiated within the neuronal environment, recruits several peripheral effectors, such as hormones (glucocorticoids, angiotensin II, estrogen), which in turn modulate central nervous system responsiveness to systemic challenges. Therefore, in this review, we shall evaluate in an integrated manner the physiological control of body fluid homeostasis from the molecular aspects to the systemic and integrated responses.

Design of automatic control system for the precipitation of bromelain from the extract of pineapple wastes

In this work, bromelain was recovered from ground pineapple stem and rind by means of precipitation with alcohol at low temperature. Bromelain is the name of a group of powerful protein-digesting, or proteolytic, enzymes that are particularly useful for reducing muscle and tissue inflammation and as a digestive aid. Temperature control is crucial to avoid irreversible protein denaturation and consequently to improve the quality of the enzyme recovered. The process was carried out alternatively in two fed-batch pilot tanks: a glass tank and a stainless steel tank. Aliquots containing 100 mL of pineapple aqueous extract were fed into the tank. Inside the jacketed tank, the protein was exposed to unsteady operating conditions during the addition of the precipitating agent (ethanol 99.5%) because the dilution ratio "aqueous extract to ethanol" and heat transfer area changed. The coolant flow rate was manipulated through a variable speed pump. Fine tuned conventional and adaptive PID controllers were on-line implemented using a fieldbus digital control system. The processing performance efficiency was enhanced and so was the quality (enzyme activity) of the product.

Underwater Remote Welding Technology for Offshore Structures

The construction of offshore structures, equipment and devices requires a high level of mechanical reliability in terms of strength, toughness and ductility. One major site for mechanical failure, the weld joint region, needs particularly careful examination, and weld joint quality has become a major focus of research in recent times. Underwater welding carried out offshore faces specific challenges affecting the mechanical reliability of constructions completed underwater. The focus of this thesis is on improvement of weld quality of underwater welding using control theory. This research work identifies ways of optimizing the welding process parameters of flux cored arc welding (FCAW) during underwater welding so as to achieve desired weld bead geometry when welding in a water environment. The weld bead geometry has no known linear relationship with the welding process parameters, which makes it difficult to determine a satisfactory weld quality. However, good weld bead geometry is achievable by controlling the welding process parameters. The doctoral dissertation comprises two sections. The first part introduces the topic of the research, discusses the mechanisms of underwater welding and examines the effect of the water environment on the weld quality of wet welding. The second part comprises four research papers examining different aspects of underwater wet welding and its control and optimization. Issues considered include the effects of welding process parameters on weld bead geometry, optimization of FCAW process parameters, and design of a control system for the purpose of achieving a desired bead geometry that can ensure a high level of mechanical reliability in welded joints of offshore structures. Artificial neural network systems and a fuzzy logic controller, which are incorporated in the control system design, and a hybrid of fuzzy and PID controllers are the major control dynamics used. This study contributes to knowledge of possible solutions for achieving similar high weld quality in underwater wet welding as found with welding in air. The study shows that carefully selected steels with very low carbon equivalent and proper control of the welding process parameters are essential in achieving good weld quality. The study provides a platform for further research in underwater welding. It promotes increased awareness of the need to improve the quality of underwater welding for offshore industries and thus minimize the risk of structural defects resulting from poor weld quality.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.

Physiological reactivity and the perception of emotional stimuli as they relate to social adaptive functioning after traumatic brain injury / y Melonie Jane Hopkins.

Traumatic brain injury (TBI) often affects social adaptive functioning and these changes in social adaptability are usually associated with general damage to the frontal cortex. Recent evidence suggests that certain neurons within the orbitofrontal cortex appear to be specialized for the processing of faces and facial expressions. The orbitofrontal cortex also appears to be involved in self-initiated somatic activation to emotionally-charged stimuli. According to Somatic Marker Theory (Damasio, 1994), the reduced physiological activation fails to provide an individual with appropriate somatic cues to personally-relevant stimuli and this, in turn, may result in maladaptive behaviour. Given the susceptibility of the orbitofrontal cortex in TBI, it was hypothesized that impaired perception and reactivity to socially-relevant information might be responsible for some of the social difficulties encountered after TBL Fifteen persons who sustained a moderate to severe brain injury were compared to age and education matched Control participants. In the first study, both groups were presented with photographs of models displaying the major emotions and either asked to identify the emotions or simply view the faces passively. In a second study, participants were asked to select cards from decks that varied in terms of how much money could be won or lost. Those decks with higher losses were considered to be high-risk decks. Electrodermal activity was measured concurrently in both situations. Relative to Controls, TBI participants were found to have difficulty identifying expressions of surprise, sadness, anger, and fear. TBI persons were also found to be under-reactive, as measured by electrodermal activity, while passively viewing slides of negative expressions. No group difference,in reactivity to high-risk card decks was observed. The ability to identify emotions in the face and electrodermal reactivity to faces and to high-risk decks in the card game were examined in relationship to social monitoring and empathy as described by family members or friends on the Brock Adaptive Functioning Questionnaire (BAFQ). Difficulties identifying negative expressions (i.e., sadness, anger, fear, and disgust) predicted problems in monitoring social situations. As well, a modest relationship was observed between hypo-arousal to negative faces and problems with social monitoring. Finally, hypo-arousal in the anticipation of risk during the card game related to problems in empathy. In summary, these data are consistent with the view that alterations in the ability to perceive emotional expressions in the face and the disruption in arousal to personally-relevant information may be accounting for some of the difficulties in social adaptation often observed in persons who have sustained a TBI. Furthermore, these data provide modest support for Damasio's Somatic Marker Theory in that physiological reactivity to socially-relevant information has some value in predicting social function. Therefore, the assessment of TBI persons, particularly those with adaptive behavioural problems, should be expanded to determine whether alterations in perception and reactivity to socially-relevant stimuli have occurred. When this is the case, rehabilitative strategies aimed more specifically at these difficulties should be considered.

Cross-Linguistic Transfer (CLT) in Bilingual Speakers : Neural Correlates of Language Learning

Le but de cette thèse est d'étudier les corrélats comportementaux et neuronaux du transfert inter-linguistique (TIL) dans l'apprentissage d’une langue seconde (L2). Compte tenu de nos connaissances sur l'influence de la distance linguistique sur le TIL (Paradis, 1987, 2004; Odlin, 1989, 2004, 2005; Gollan, 2005; Ringbom, 2007), nous avons examiné l'effet de facilitation de la similarité phonologique à l’aide de la résonance magnétique fonctionnelle entre des langues linguistiquement proches (espagnol-français) et des langues linguistiquement éloignées (persan-français). L'étude I rapporte les résultats obtenus pour des langues linguistiquement proches (espagnol-français), alors que l'étude II porte sur des langues linguistiquement éloignées (persan-français). Puis, les changements de connectivité fonctionnelle dans le réseau langagier (Price, 2010) et dans le réseau de contrôle supplémentaire impliqué dans le traitement d’une langue seconde (Abutalebi & Green, 2007) lors de l’apprentissage d’une langue linguistiquement éloignée (persan-français) sont rapportés dans l’étude III. Les résultats des analyses d’IRMF suivant le modèle linéaire général chez les bilingues de langues linguistiquement proches (français-espagnol) montrent que le traitement des mots phonologiquement similaires dans les deux langues (cognates et clangs) compte sur un réseau neuronal partagé par la langue maternelle (L1) et la L2, tandis que le traitement des mots phonologiquement éloignés (non-clang-non-cognates) active des structures impliquées dans le traitement de la mémoire de travail et d'attention. Toutefois, chez les personnes bilingues de L1-L2 linguistiquement éloignées (français-persan), même les mots phonologiquement similaires à travers les langues (cognates et clangs) activent des régions connues pour être impliquées dans l'attention et le contrôle cognitif. Par ailleurs, les mots phonologiquement éloignés (non-clang-non-cognates) activent des régions usuellement associées à la mémoire de travail et aux fonctions exécutives. Ainsi, le facteur de distance inter-linguistique entre L1 et L2 module la charge cognitive sur la base du degré de similarité phonologiques entres les items en L1 et L2. Des structures soutenant les processus impliqués dans le traitement exécutif sont recrutées afin de compenser pour des demandes cognitives. Lorsque la compétence linguistique en L2 augmente et que les tâches linguistiques exigent ainsi moins d’effort, la demande pour les ressources cognitives diminue. Tel que déjà rapporté (Majerus, et al, 2008; Prat, et al, 2007; Veroude, et al, 2010; Dodel, et al, 2005; Coynel, et al ., 2009), les résultats des analyses de connectivité fonctionnelle montrent qu’après l’entraînement la valeur d'intégration (connectivité fonctionnelle) diminue puisqu’il y a moins de circulation du flux d'information. Les résultats de cette recherche contribuent à une meilleure compréhension des aspects neurocognitifs et de plasticité cérébrale du TIL ainsi que l'impact de la distance linguistique dans l'apprentissage des langues. Ces résultats ont des implications dans les stratégies d'apprentissage d’une L2, les méthodes d’enseignement d’une L2 ainsi que le développement d'approches thérapeutiques chez des patients bilingues qui souffrent de troubles langagiers.